Tag: Danilo Poccia

With Agents for Amazon Bedrock, applications can use generative artificial intelligence (generative AI) to run tasks across multiple systems and data sources. Starting today, these new capabilities streamline the creation and management of agents: Quick agent creation – You can now quickly create an agent and optionally add instructions and action groups later, providing flexibility and agility for your development process. Agent builder – All agent configurations can be operated in the new agent builder section of the console. Simplified configuration – Action groups can use a simplified schema that just lists functions and parameters without having to provide an API schema. Return of control –You can skip using an AWS Lambda function and return control to the application invoking…

With Amazon Bedrock, you have access to a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies that make it easier to build and scale generative AI applications. Some of these models provide publicly available weights that can be fine-tuned and customized for specific use cases. However, deploying customized FMs in a secure and scalable way is not an easy task. Starting today, Amazon Bedrock adds in preview the capability to import custom weights for supported model architectures (such as Meta Llama 2, Llama 3, and Mistral) and serve the custom model using On-Demand mode. You can import models with weights in Hugging Face safetensors format from Amazon SageMaker and Amazon Simple Storage Service (Amazon S3). In…

Industries like automotive, robotics, and finance are increasingly implementing computational workloads like simulations, machine learning (ML) model training, and big data analytics to improve their products. For example, automakers rely on simulations to test autonomous driving features, robotics companies train ML algorithms to enhance robot perception capabilities, and financial firms run in-depth analyses to better manage risk, process transactions, and detect fraud. Some of these workloads, including simulations, are especially complicated to run due to their diversity of components and intensive computational requirements. A driving simulation, for instance, involves generating 3D virtual environments, vehicle sensor data, vehicle dynamics controlling car behavior, and more. A robotics simulation might test hundreds of autonomous delivery robots interacting with each other and other systems…

This has been a busy week – we introduced a new kind of Amazon CloudFront infrastructure, more efficient ways to analyze data stored on Amazon Simple Storage Service (Amazon S3), and new generative AI capabilities. Last week’s launches Here’s what got my attention: Amazon Bedrock – Mistral AI’s Mixtral 8x7B and Mistral 7B foundation models are now generally available on Amazon Bedrock. More details in Donnie’s post. Here’s a deep dive into Mistral 7B and Mixtral 8x7B models, by my colleague Mike. Knowledge Bases for Amazon Bedrock – With hybrid search support, you can improve the relevance of retrieved results, especially for keyword searches. More information and examples in this post on the AWS Machine Learning Blog. Amazon CloudFront –…